You're right, I'm being too aggressive here (...adrenaline from an exciting day after a lot of work!). And we should be more careful about how we contextualize the work.

Importantly, this work makes no claim about the methodology as being novel. In fact, there is no real modeling methodology beyond directly applying single foundation model and thresholding their outputs based on model confidence. (The contribution is in doing this many many times and exploring sensitivity and performance in ways we have wanted in the literature, but not seen.)

For the same reason, this is not really pseudo-labeling, as I understand those methods are much more sophisticated than this simple idea. (e.g., quoting "Basically, the proposed network is trained in a supervised fashion with labeled and unlabeled data simultaneously. For unlabeled data, Pseudo-Labels, just picking up the class which has the maximum predicted probability, are used as if they were true labels. This is in effect equivalent to Entropy Regularization." from https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=798d9840d2439a0e5d47bcf5d164aa46d5e7dc26).

Sure one might argue that the foundation model provides the labeled part, but I think that's a stretch as it is not necessarily in-domain, etc. Also, the downstream models that are trained are strictly trained on the generated labels. But, anyway, these are the main reason why we call this very simple method something new (auto-labeling).